PIPP Phase I: Real-time Analytics to Monitor and Predict Emerging Plant Disease

PIPP 第一阶段：实时分析监测和预测新发植物病害

• 批准号: 2200038
• 负责人: Jean Ristaino
• 金额: $ 100万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200038&HistoricalAwards=false
• 关键词: PIPP; Phase; Real; time; Analytics

Plant disease outbreaks are increasing and threatening food security for the vulnerable in many areas of the world and in the US. A stable, nutritious food supply is needed to both lift people out of poverty and improve health outcomes. Plant diseases cause crop losses from 20% to 30% in staple food crops. Plant diseases, both common and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, and emergence of new strains that may be difficult to control. This team of researchers will develop better ways to detect and predict when and where plant diseases will emerge. This research will characterize how human attitudes and social behavior of stakeholders impacts plant disease transmission and adoption of sensor, surveillance and disease prediction technologies. The team will engage a diverse group of postdoctoral associates, graduate students and research staff through research and workshop participation and foster partnerships for a future Plant Disease Pandemic Preparedness Center.Prediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance, and data analytics to inform decision-making and prevent spread. This is the grand challenge that the convergence research team will tackle in this Predictive Intelligence for Pandemic Prevention (PIPP) planning grant. In order to improve pandemic prediction and tackle this grand challenge, a new set of predictive tools is needed. In the PIPP Phase I project, the multidisciplinary team will develop a pandemic prediction system called the “Plant Aid Database (PAdb)” that links pathogen detection by in-situ plant disease sensors and remote sensing of crop health, genomic surveillance, real-time spatial and temporal data analytics and climate data to develop predictive simulations of plant disease pandemics. The team plans to validate the PAdb using several model plant pathogens including novel lineages of Phytophthora infestans and the cucurbit downy mildew pathogen Pseudoperonospora cubensis. They plan to engage a broad group of stakeholders including scientists, growers, extension specialists, the USDA APHIS Plant Protection and Quarantine personnel, the Department of Homeland Security inspectors, and diagnosticians in the National Plant Diagnostic Network in a Pandemic Preparedness workshop. Differences in response and spread of pathogens and stakeholder experiences will be examined using current methods and the aid of the new PAdb.This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在世界许多地区和美国，植物性疾病暴发正在增加和威胁脆弱的粮食安全。需要稳定，营养丰富的食物供应，以使人们摆脱贫困并改善健康状况。植物性疾病导致主食粮食作物的农作物损失从20％到30％。由于气候变化，全球粮食贸易网络的传播以及可能难以控制的新菌株的出现，植物疾病（无论是常见还是最近出现）正在扩散和加剧。该研究人员将开发更好的方法来检测和预测何时以及何时出现植物疾病。这项研究将表征人类参与者和利益相关者的社会行为如何影响植物疾病的传播和传感器，监视和疾病预测技术的采用。该团队将通过研究和研讨会参与以及促进伙伴关系来与未来的植物疾病大流行准备中心建立一组潜水员，研究生和研究人员。预定植物性疾病大流行病是由于缺乏实时检测，监视，监视，以及数据分析以告知决策和扩散的数据，这是不可靠的。这是融合研究团队将在这项大​​流行预防（PIPP）计划赠款的预测情报中应对的巨大挑战。为了改善大流行预测并应对这一巨大挑战，需要一组新的预测工具。在PIPP第一阶段项目中，多学科团队将开发一种名为“植物援助数据库（PADB）”的大流行预测系统，该系统通过原位植物疾病传感器与作物健康，基因组监测，真实空间和临时数据分析和临时数据分析和气候数据的远程感知将病原体检测联系起来，以开发植物性疾病的预测性模拟。该团队计划使用几种模型植物病原体来验证PADB，包括植物疫霉菌的新谱系和Cucurbit down Millew病原体伪孢子虫cubensis。他们计划吸引一群利益相关者，包括科学家，种植者，推广专家，USDA APHIS植物保护和隔离人员，国土安全检查员部以及全国植物诊断网络中的诊断师在大流行准备工作室中。 Differences in Response and spread of pathogens and stakeholder experiences will be examined using current methods and the aid of the new PAdb.This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sc​​iences （SBE）。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是宝贵的支持。

项目成果

CRISPR‐Cas Biochemistry and CRISPR‐Based Molecular Diagnostics

CRISPR-Cas 生物化学和基于 CRISPR-的分子诊断

• DOI: 10.1002/anie.202214987
• 发表时间: 2023
• 期刊: Angewandte Chemie International edition in English
• 作者: Weng, Zhengyan;You, Zheng;Yang, Jie;Mohammad, Noor;Lin, Mengshi;Wei, Qingshan;Gao, Xue;Zhang, Yi
• 通讯作者: Zhang, Yi

Understanding the genotypic and phenotypic structure and impact of climate on Phytophthora nicotianae outbreaks on potato and tomato in the eastern US

了解基因型和表型结构以及气候对美国东部马铃薯和番茄疫霉爆发的影响

• DOI: 10.1094/phyto-11-22-0411-r
• 发表时间: 2023
• 期刊: Phytopathology®
• 作者: Saville, Amanda;McGrath, Margaret;Jones, Christopher;Polo, John;Ristaino, Jean B.
• 通讯作者: Ristaino, Jean B.

Plant pest invasions, as seen through news and social media

• DOI: 10.1016/j.compenvurbsys.2022.101922
• 发表时间: 2022-12-28
• 期刊: COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
• 影响因子: 6.8
• 作者: Tateosian，Laura G.;Saffer，Ariel;Shukunobe，Makiko
• 通讯作者: Shukunobe，Makiko